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arXiv · 2205.11692

TAILOR: Teaching with Active and Incremental Learning for Object Registration

Qianli Xu·Nicolas Gauthier·Wenyu Liang·Fen Fang·Hui Li Tan·Ying Sun·Yan Wu·Liyuan Li·Joo-Hwee Lim
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Abstract

When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and labor-intensive. We present TAILOR -- a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions.

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Qianli Xu, Nicolas Gauthier, Wenyu Liang, Fen Fang, Hui Li Tan, Ying Sun, Yan Wu, Liyuan Li, Joo-Hwee Lim. 2022-05-24. TAILOR: Teaching with Active and Incremental Learning for Object Registration. https://arxiv.org/abs/2205.11692

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